RiboDiffusion: Tertiary Structure-based RNA Inverse Folding with Generative Diffusion Models

1 code implementation • 17 Apr 2024 • Han Huang, Ziqian Lin, Dongchen He, Liang Hong, Yu Li

A fundamental challenge is to find functional RNA sequences that satisfy given structural constraints, known as the inverse folding problem.

Dual Operating Modes of In-Context Learning

1 code implementation • 29 Feb 2024 • Ziqian Lin, Kangwook Lee

We introduce a probabilistic model, with which one can explain the dual operating modes of ICL simultaneously.

In-Context Learning Retrieval

Pre-trained Recommender Systems: A Causal Debiasing Perspective

1 code implementation • 30 Oct 2023 • Ziqian Lin, Hao Ding, Nghia Trong Hoang, Branislav Kveton, Anoop Deoras, Hao Wang

In particular, we propose to develop a generic recommender that captures universal interaction patterns by training on generic user-item interaction data extracted from different domains, which can then be fast adapted to improve few-shot learning performance in unseen new domains (with limited data).

Few-Shot Learning Recommendation Systems

LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks

1 code implementation • 14 Jun 2022 • Tuan Dinh, Yuchen Zeng, Ruisu Zhang, Ziqian Lin, Michael Gira, Shashank Rajput, Jy-yong Sohn, Dimitris Papailiopoulos, Kangwook Lee

LIFT does not make any changes to the model architecture or loss function, and it solely relies on the natural language interface, enabling "no-code machine learning with LMs."

BIG-bench Machine Learning General Classification +2

MOOD: Multi-level Out-of-distribution Detection

1 code implementation • CVPR 2021 • Ziqian Lin, Sreya Dutta Roy, Yixuan Li

Out-of-distribution (OOD) detection is essential to prevent anomalous inputs from causing a model to fail during deployment.

Out-of-Distribution Detection Out of Distribution (OOD) Detection